Multi-feature fusion 3D-CNN for tooth segmentation

Yunbo Rao; Miao Gou; Yilin Wang; Zening Chen; Junmin Xue; Jianxun Sun; Zairong Wang

doi:10.1117/12.2589905

27 January 2021 Multi-feature fusion 3D-CNN for tooth segmentation

Yunbo Rao, Miao Gou, Yilin Wang, Zening Chen, Junmin Xue, Jianxun Sun, Zairong Wang

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 1172010 (2021) https://doi.org/10.1117/12.2589905
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Semantic segmentation on medical Computed Tomography (CT) images is of great significance to research and clinical diagnosis. However, methods based on neural network have competitive advantages for segmentation of dental CT images. In this paper, a 3D multi-feature fusion method for tooth segmentation is proposed. In order to obtain the body space of the data, first of all, the dental CT training set is compressed in NII format, and the body space data is processed; then the proposed 3D convolution network is used to train the data, extract the feature vectors, and obtain the probability distribution; to handle the situation that 3D neural network always leads to fuzzy boundary and unclear topology, the new CRF algorithm is used to refine the probability distribution which removes the redundant information generated by the neural network model, and makes the segmentation results more accurate. Compared with diverse contemporary segmentation algorithms, the effectiveness and superiority of our proposed method are verified, proving the conclusion that the supervision mechanism, neural network model components, and optimization proposed methods can improve the accuracy of tooth segmentation is reliable and valid.

Citation Download Citation

Yunbo Rao, Miao Gou, Yilin Wang, Zening Chen, Junmin Xue, Jianxun Sun, and Zairong Wang "Multi-feature fusion 3D-CNN for tooth segmentation", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 1172010 (27 January 2021); https://doi.org/10.1117/12.2589905

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